Harbor Adapters and Harbor-Index (previously named as harbor-mix) accepted at NeurIPS E&D! Congrats and special thanks to all of our contributors, advisors, and funding partners!
As nearly all AGI labs are using harbor format, we’re really honored and proud to build the bridge. Standardization is never easy, but helps the most.
For more details, full paper, and contribution list, see xitter.cf/linshi592021/status/20…
Replying to @LinShi592021
Join us on Discord for future work and discussions: discord.gg/QVvyhRw5UQ
Related links:
- Harbor Framework: harborframework.com/
- GitHub: github.com/harbor-framework/… and github.com/harbor-framework/…
- Harbor Adapters: harborframework.com/docs/dat…
- Harbor Hub: hub.harborframework.com/
Harbor Adapters and Harbor-Index
arxiv.org/pdf/2609.04298
1+year work, 120+contributors, 300+PRs, 10+ funding partners.
Huge thanks to our funding partners (@OpenAI @AnthropicAI @GoogleDeepMind @Alibaba_Qwen @deepseek_ai @Kimi_Moonshot @Zai_org @MiniMax_AI @XiaomiMiMo @LaudeInstitute
@2077AI @UniPat_AI ), sandbox providers (
@daytonaio @modal ), and our 120+ contributors and advisors from @terminalbench and @harborframework who brought the work to life!
Join us on Discord for future work and discussions: discord.gg/QVvyhRw5UQ
Related links:
- Harbor Framework: harborframework.com/
- GitHub: github.com/harbor-framework/… and github.com/harbor-framework/…
- Harbor Adapters: harborframework.com/docs/dat…
- Harbor Hub: hub.harborframework.com/
Introducing Harbor-Index, a compact, diverse, and high-quality benchmark built to challenge frontier agents.
We carefully select, audit and fix 82 high-signal tasks out of 6,627 candidates spanning 54 benchmarks.
No agent gets above 30%. (1/5)
Every task is solidified through multiple iterations of audits and patches from LLM judges and human reviewers.
As a result, we removed broken tasks so failures reflect genuine capability gaps. We also address known reward hacks: out of 1,476 runs, only 9 managed to cheat. (4/5)
Harbor-Index is live. Browse all 82 tasks and 1,476 rollouts at harbor-index.org
Huge thanks to our funding partners (@OpenAI @AnthropicAI @GoogleDeepMind @Alibaba_Qwen @deepseek_ai @Kimi_Moonshot, @Zai_org, @MiniMax_AI, @XiaomiMiMo, @LaudeInstitute, @2077AI, @UniPat_AI), sandbox providers (@daytonaio, @modal), and our 120 contributors and advisors from @terminalbench and @harborframework who brought Harbor-Index to life!
And finally, our deepest thanks to the open benchmark community that made Harbor-Index possible: @alexgshaw, @ryan_marten, @ekellbuch, @longphan3110, @slimshetty_, @ori_press, @fchollet, @ludomitch, @froger_romain, @ChowdhuryNeil, Qixing Zhou, @jonmlaurent, Xiang Deng, @mialon_gregoire, @MinyangTian1, @Mike_A_Merrill, Zhun Wang, Bofei Gao, @xdotli, @fangyu_lei, @samuelp1002, @terryyuezhuo, @Shanda_Li_2000, @yeeelow233, @idavidrein, Rui Yang, @christinexye, @AndyLin2001, @jyangballin, @nielstron, @BenShi34, Ryan Wong, and so many more benchmark authors.
Join us to build the next iteration of Harbor-Index on discord.com/invite/2Pe5uWGcV…!
CAIS AgenticSE Workshop Keynote Talk: Harbor Adapters & Harbor Index
30min video here:
youtu.be/tvAAKX2XVus?si=84L2…
Happy to be invited as a keynote speaker and present our recent study on Harbor Adapters and agentic evaluation!
Lin Shi retweeted
The Harbor registry is getting an upgrade.
Now, anyone can publish to the registry to make their dataset available to every Harbor user:
Lin Shi retweeted
Terminal-Bench is a leading benchmark for agents. Unfortunately it’s hard: most small coding agents get very low scores on TB2, so training/system ablations look flat - you can't tell what's working.
Announcing OpenThoughts-TBLite - 100 curated TB2-style tasks, difficulty-calibrated so even 8B models can make progress. It's designed to give researchers measurable signal during development, providing faster feedback for experimental iteration while closely tracking true TB2 performance🧵
Lin Shi retweeted
A new Harbor benchmark comprised of tasks from @SnorkelAI @bespokelabsai and Terminal-Bench 2.0 rejects due to difficulty.
Useful for hillclimbing TB tasks with small models.
Terminal-Bench is a leading benchmark for agents. Unfortunately it’s hard: most small coding agents get very low scores on TB2, so training/system ablations look flat - you can't tell what's working.
Announcing OpenThoughts-TBLite - 100 curated TB2-style tasks, difficulty-calibrated so even 8B models can make progress. It's designed to give researchers measurable signal during development, providing faster feedback for experimental iteration while closely tracking true TB2 performance🧵
Lin Shi retweeted
Come help us build Terminal-Bench 3.0! It will be an extremely high quality, difficult benchmark.
Replying to @ryan_marten
if you want to help us create harder tasks, come join the terminal-bench-3.0 effort
discord.gg/6xWPKhGDbA














